Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA.
Analyst. 2021 Jun 28;146(13):4135-4145. doi: 10.1039/d1an00060h.
Amyloid aggregation, formed by aberrant proteins, is a pathological hallmark for neurodegenerative diseases, including Alzheimer's disease and Huntington's disease. High-resolution holistic mapping of the fine structures from these aggregates should facilitate our understanding of their pathological roles. Here, we achieved label-free high-resolution imaging of the polyQ and the amyloid-beta (Aβ) aggregates in cells and tissues utilizing a sample-expansion stimulated Raman strategy. We further focused on characterizing the Aβ plaques in 5XFAD mouse brain tissues. 3D volumetric imaging enabled visualization of the whole plaques, resolving both the fine protein filaments and the surrounding components. Coupling our expanded label-free Raman imaging with machine learning, we obtained specific segmentation of aggregate cores, peripheral filaments together with cell nuclei and blood vessels by pre-trained convolutional neural network models. Combining with 2-channel fluorescence imaging, we achieved a 6-color holistic view of the same sample. This ability for precise and multiplex high-resolution imaging of the protein aggregates and their micro-environment without the requirement of labeling would open new biomedical applications.
淀粉样蛋白聚集物由异常蛋白质形成，是神经退行性疾病（包括阿尔茨海默病和亨廷顿病）的病理标志。对这些聚集物的精细结构进行高分辨率整体测绘，有助于我们了解它们的病理作用。在这里，我们利用样品扩展刺激拉曼策略，实现了对细胞和组织中聚 Q 和淀粉样 β（Aβ）聚集物的无标记高分辨率成像。我们进一步专注于表征 5XFAD 小鼠脑组织中的 Aβ斑块。3D 体积成像使我们能够可视化整个斑块，解析出精细的蛋白质丝和周围成分。通过预先训练的卷积神经网络模型，我们将扩展的无标记拉曼成像与机器学习相结合，获得了对聚集核、外围丝以及细胞核和血管的特定分割。结合双通道荧光成像，我们实现了对同一样本的 6 种颜色整体观察。这种无需标记即可精确、多路复用高分辨率成像蛋白质聚集物及其微环境的能力将开辟新的生物医学应用。
